Disseminating STEM Subjects and Ocean Literacy through a Bioinspired Toolkit.

Over the last decade, education has been evolving to equip students with the fundamental skills required to cope with the challenges of sustainability and inclusivity, such as quality education, access to clean water, cultural heritage preservation and protection of marine life. Technology supports the learning process by providing useful tools that enrich the learning environment, encourage active participation, improve collaboration and prepare students for their future life. Educational Robotics is one of the most popular innovative methodologies that supports the development of many skills by assembling and programming robots in a meaningful way. In this paper, the authors aim at advancing their previous work in the field of Educational Robotics applied to the marine environment by proposing a novel bioinspired educational toolkit whose design and features support activities concerning sustainability, ocean literacy, as well as STEM subjects in kindergarten through to grade twelve education. Exploiting the established educational theories and methodologies underpinning Educational Robotics, the toolkit allows for marine-themed activities, as well promoting activities concerning STEM subjects. To explain the relevance of the toolkit, the authors present the robot design, the workshops that every teacher or student can explore as an Open Educational Resource (OERs), and the results of a case study. Interestingly, the latter shows that the use of the toolkit seems to have complemented the students' initial keen interest in technology itself, with awareness about urgent issues related to the climate and the environment.

Identification of the Students Learning Process During Education Robotics Activities.

This paper presents the design of an assessment process and its outcomes to investigate the impact of Educational Robotics activities on students' learning. Through data analytics techniques, the authors will explore the activities' output from a pedagogical and quantitative point of view. Sensors are utilized in the context of an Educational Robotics activity to obtain a more effective robot-environment interaction. Pupils work on specific exercises to make their robot smarter and to carry out more complex and inspirational projects: the integration of sensors on a robotic prototype is crucial, and learners have to comprehend how to use them. In the presented study, the potential of Educational Data Mining is used to investigate how a group of primary and secondary school students, using visual programming (Lego Mindstorms EV3 Education software), design programming sequences while they are solving an exercise related to an ultrasonic sensor mounted on their robotic artifact. For this purpose, a tracking system has been designed so that every programming attempt performed by students' teams is registered on a log file and stored in an SD card installed in the Lego Mindstorms EV3 brick. These log files are then analyzed using machine learning techniques (k-means clustering) in order to extract different patterns in the creation of the sequences and extract various problem-solving pathways performed by students. The difference between problem-solving pathways with respect to an indicator of early achievement is studied.